Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations4592
Missing cells108
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 MiB
Average record size in memory569.3 B

Variable types

Text4
Numeric13
Categorical3

Alerts

52 Weeks High is highly overall correlated with 52 Weeks Low and 6 other fieldsHigh correlation
52 Weeks Low is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
Currency is highly overall correlated with EPS AnnualHigh correlation
EPS Annual is highly overall correlated with 52 Weeks High and 7 other fieldsHigh correlation
Market Cap (in M) is highly overall correlated with 52 Weeks High and 10 other fieldsHigh correlation
Performance (52 weeks) is highly overall correlated with Market Cap (in M) and 1 other fieldsHigh correlation
Price is highly overall correlated with 52 Weeks High and 7 other fieldsHigh correlation
Price 52 Weeks Ago is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
ROI Annual is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
Résultat net is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
Total assets is highly overall correlated with Market Cap (in M) and 2 other fieldsHigh correlation
Volume 1 month is highly overall correlated with Market Cap (in M) and 2 other fieldsHigh correlation
Volume 52 weeks is highly overall correlated with Market Cap (in M) and 2 other fieldsHigh correlation
Currency is highly imbalanced (91.6%)Imbalance
Total assets has 108 (2.4%) missing valuesMissing
Price is highly skewed (γ1 = 26.93344746)Skewed
Market Cap (in M) is highly skewed (γ1 = 24.47766101)Skewed
Volume 52 weeks is highly skewed (γ1 = 43.39669932)Skewed
Volume 1 month is highly skewed (γ1 = 27.48945195)Skewed
52 Weeks High is highly skewed (γ1 = 34.20999561)Skewed
52 Weeks Low is highly skewed (γ1 = 23.19378508)Skewed
Price 52 Weeks Ago is highly skewed (γ1 = 33.94622584)Skewed
EPS Annual is highly skewed (γ1 = 48.34321231)Skewed
ROI Annual is highly skewed (γ1 = -51.82124009)Skewed
Symbol has unique valuesUnique
Company Name has unique valuesUnique
Market Cap (in M) has unique valuesUnique
Beta has unique valuesUnique

Reproduction

Analysis started2024-08-12 18:25:56.106992
Analysis finished2024-08-12 18:26:13.764745
Duration17.66 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Symbol
Text

UNIQUE 

Distinct4592
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size272.0 KiB
2024-08-12T20:26:14.039551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.6339286
Min length1

Characters and Unicode

Total characters16687
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4592 ?
Unique (%)100.0%

Sample

1st rowTRNS
2nd rowACRV
3rd rowCOLM
4th rowZCMD
5th rowMOVE
ValueCountFrequency (%)
trns 1
 
< 0.1%
rrbi 1
 
< 0.1%
zcmd 1
 
< 0.1%
move 1
 
< 0.1%
nmih 1
 
< 0.1%
gnta 1
 
< 0.1%
allr 1
 
< 0.1%
aosl 1
 
< 0.1%
kins 1
 
< 0.1%
itri 1
 
< 0.1%
Other values (4582) 4582
99.8%
2024-08-12T20:26:14.468459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1191
 
7.1%
C 1171
 
7.0%
T 1117
 
6.7%
S 1110
 
6.7%
R 1092
 
6.5%
N 970
 
5.8%
L 856
 
5.1%
I 845
 
5.1%
E 763
 
4.6%
M 759
 
4.5%
Other values (16) 6813
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16687
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1191
 
7.1%
C 1171
 
7.0%
T 1117
 
6.7%
S 1110
 
6.7%
R 1092
 
6.5%
N 970
 
5.8%
L 856
 
5.1%
I 845
 
5.1%
E 763
 
4.6%
M 759
 
4.5%
Other values (16) 6813
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16687
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1191
 
7.1%
C 1171
 
7.0%
T 1117
 
6.7%
S 1110
 
6.7%
R 1092
 
6.5%
N 970
 
5.8%
L 856
 
5.1%
I 845
 
5.1%
E 763
 
4.6%
M 759
 
4.5%
Other values (16) 6813
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16687
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1191
 
7.1%
C 1171
 
7.0%
T 1117
 
6.7%
S 1110
 
6.7%
R 1092
 
6.5%
N 970
 
5.8%
L 856
 
5.1%
I 845
 
5.1%
E 763
 
4.6%
M 759
 
4.5%
Other values (16) 6813
40.8%

Company Name
Text

UNIQUE 

Distinct4592
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 KiB
2024-08-12T20:26:14.675703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length55
Median length39
Mean length19.682274
Min length2

Characters and Unicode

Total characters90381
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4592 ?
Unique (%)100.0%

Sample

1st rowTranscat Inc
2nd rowAcrivon Therapeutics Inc
3rd rowColumbia Sportswear Co
4th rowZhongchao Inc
5th rowMovano Inc
ValueCountFrequency (%)
inc 2899
 
20.8%
corp 839
 
6.0%
ltd 361
 
2.6%
holdings 344
 
2.5%
group 260
 
1.9%
co 188
 
1.4%
therapeutics 178
 
1.3%
financial 123
 
0.9%
technologies 113
 
0.8%
bancorp 108
 
0.8%
Other values (4865) 8508
61.1%
2024-08-12T20:26:14.994286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9329
 
10.3%
n 7728
 
8.6%
e 6080
 
6.7%
o 5730
 
6.3%
c 5468
 
6.0%
r 5296
 
5.9%
i 5129
 
5.7%
a 5054
 
5.6%
t 4149
 
4.6%
s 3630
 
4.0%
Other values (64) 32788
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90381
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9329
 
10.3%
n 7728
 
8.6%
e 6080
 
6.7%
o 5730
 
6.3%
c 5468
 
6.0%
r 5296
 
5.9%
i 5129
 
5.7%
a 5054
 
5.6%
t 4149
 
4.6%
s 3630
 
4.0%
Other values (64) 32788
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90381
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9329
 
10.3%
n 7728
 
8.6%
e 6080
 
6.7%
o 5730
 
6.3%
c 5468
 
6.0%
r 5296
 
5.9%
i 5129
 
5.7%
a 5054
 
5.6%
t 4149
 
4.6%
s 3630
 
4.0%
Other values (64) 32788
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90381
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9329
 
10.3%
n 7728
 
8.6%
e 6080
 
6.7%
o 5730
 
6.3%
c 5468
 
6.0%
r 5296
 
5.9%
i 5129
 
5.7%
a 5054
 
5.6%
t 4149
 
4.6%
s 3630
 
4.0%
Other values (64) 32788
36.3%

Price
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3404
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.60512
Minimum0.0503
Maximum8506.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.0 KiB
2024-08-12T20:26:15.118468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0503
5-th percentile0.61451
Q13.35
median13.03
Q344.42
95-th percentile198.3335
Maximum8506.24
Range8506.1897
Interquartile range (IQR)41.07

Descriptive statistics

Standard deviation179.89764
Coefficient of variation (CV)3.5549297
Kurtosis1120.7504
Mean50.60512
Median Absolute Deviation (MAD)11.665
Skewness26.933447
Sum232378.71
Variance32363.162
MonotonicityNot monotonic
2024-08-12T20:26:15.232152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.06 8
 
0.2%
1.01 8
 
0.2%
1.4 8
 
0.2%
1.65 8
 
0.2%
1.6 7
 
0.2%
1.02 7
 
0.2%
11.45 7
 
0.2%
1.47 7
 
0.2%
1.51 7
 
0.2%
2 6
 
0.1%
Other values (3394) 4519
98.4%
ValueCountFrequency (%)
0.0503 1
< 0.1%
0.075 1
< 0.1%
0.076 1
< 0.1%
0.0766 1
< 0.1%
0.08 1
< 0.1%
0.0823 1
< 0.1%
0.092 1
< 0.1%
0.0933 1
< 0.1%
0.0945 1
< 0.1%
0.0978 1
< 0.1%
ValueCountFrequency (%)
8506.24 1
< 0.1%
3443.05 1
< 0.1%
3120.25 1
< 0.1%
1974.15 1
< 0.1%
1883.62 1
< 0.1%
1752.25 1
< 0.1%
1701.48 1
< 0.1%
1521.92 1
< 0.1%
1397.26 1
< 0.1%
1259.41 1
< 0.1%

Market Cap (in M)
Real number (ℝ)

HIGH CORRELATION  SKEWED  UNIQUE 

Distinct4592
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12309.368
Minimum0.20246208
Maximum3287742.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.0 KiB
2024-08-12T20:26:15.335053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.20246208
5-th percentile6.3694738
Q192.732213
median673.36585
Q34002.716
95-th percentile41955.022
Maximum3287742.5
Range3287742.3
Interquartile range (IQR)3909.9838

Descriptive statistics

Standard deviation95367.004
Coefficient of variation (CV)7.7475145
Kurtosis709.27135
Mean12309.368
Median Absolute Deviation (MAD)658.48583
Skewness24.477661
Sum56524616
Variance9.0948654 × 109
MonotonicityNot monotonic
2024-08-12T20:26:15.541882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1051.552692 1
 
< 0.1%
17762.63656 1
 
< 0.1%
6727.872229 1
 
< 0.1%
685.544 1
 
< 0.1%
3819.010817 1
 
< 0.1%
11305.98031 1
 
< 0.1%
13096.08757 1
 
< 0.1%
327.6875743 1
 
< 0.1%
76.92930363 1
 
< 0.1%
6350.50102 1
 
< 0.1%
Other values (4582) 4582
99.8%
ValueCountFrequency (%)
0.202462083 1
< 0.1%
0.290874472 1
< 0.1%
0.3662907246 1
< 0.1%
0.5117001326 1
< 0.1%
0.6849636886 1
< 0.1%
0.6859719465 1
< 0.1%
0.727851 1
< 0.1%
0.7453370722 1
< 0.1%
0.7492588667 1
< 0.1%
0.7598715686 1
< 0.1%
ValueCountFrequency (%)
3287742.486 1
< 0.1%
3017962.34 1
< 0.1%
2581647.096 1
< 0.1%
2024988.839 1
< 0.1%
1752130.051 1
< 0.1%
1309863.215 1
< 0.1%
847457.4806 1
< 0.1%
690133.0242 1
< 0.1%
638928.0644 1
< 0.1%
585534.9078 1
< 0.1%

Beta
Real number (ℝ)

UNIQUE 

Distinct4592
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0581903
Minimum-6.9775743
Maximum19.570795
Zeros0
Zeros (%)0.0%
Negative448
Negative (%)9.8%
Memory size36.0 KiB
2024-08-12T20:26:15.653140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-6.9775743
5-th percentile-0.23997649
Q10.43572978
median0.92880535
Q31.5496012
95-th percentile2.8718576
Maximum19.570795
Range26.548369
Interquartile range (IQR)1.1138714

Descriptive statistics

Standard deviation1.0876713
Coefficient of variation (CV)1.0278599
Kurtosis24.454103
Mean1.0581903
Median Absolute Deviation (MAD)0.54784378
Skewness1.7977748
Sum4859.2097
Variance1.183029
MonotonicityNot monotonic
2024-08-12T20:26:15.764401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9319894 1
 
< 0.1%
0.84577984 1
 
< 0.1%
0.19360098 1
 
< 0.1%
1.2201371 1
 
< 0.1%
0.5152113 1
 
< 0.1%
2.1184125 1
 
< 0.1%
1.8902502 1
 
< 0.1%
1.438754 1
 
< 0.1%
1.555489 1
 
< 0.1%
0.74494594 1
 
< 0.1%
Other values (4582) 4582
99.8%
ValueCountFrequency (%)
-6.9775743 1
< 0.1%
-5.8663783 1
< 0.1%
-4.9759016 1
< 0.1%
-4.3898983 1
< 0.1%
-3.8894632 1
< 0.1%
-3.6600218 1
< 0.1%
-3.375102 1
< 0.1%
-3.0445251 1
< 0.1%
-3.043933 1
< 0.1%
-2.9128923 1
< 0.1%
ValueCountFrequency (%)
19.570795 1
< 0.1%
11.313087 1
< 0.1%
9.652831 1
< 0.1%
8.4484005 1
< 0.1%
8.446643 1
< 0.1%
7.075054 1
< 0.1%
7.0186133 1
< 0.1%
6.7305875 1
< 0.1%
6.5846024 1
< 0.1%
5.979147 1
< 0.1%

Volume 52 weeks
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4590
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1531077.9
Minimum156.57371
Maximum4.6049593 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.0 KiB
2024-08-12T20:26:15.874950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum156.57371
5-th percentile12172.103
Q1100339.38
median410069.06
Q31212900.2
95-th percentile5458151.1
Maximum4.6049593 × 108
Range4.6049577 × 108
Interquartile range (IQR)1112560.8

Descriptive statistics

Standard deviation7948036.6
Coefficient of variation (CV)5.191138
Kurtosis2434.055
Mean1531077.9
Median Absolute Deviation (MAD)365140.49
Skewness43.396699
Sum7.0307097 × 109
Variance6.3171286 × 1013
MonotonicityNot monotonic
2024-08-12T20:26:15.981189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225453.9683 2
 
< 0.1%
6648.015873 2
 
< 0.1%
50909.83794 1
 
< 0.1%
484711.1111 1
 
< 0.1%
1923534.921 1
 
< 0.1%
168332.5397 1
 
< 0.1%
22885.15873 1
 
< 0.1%
339926.9841 1
 
< 0.1%
303901.9841 1
 
< 0.1%
1501179.365 1
 
< 0.1%
Other values (4580) 4580
99.7%
ValueCountFrequency (%)
156.5737052 1
< 0.1%
665.0873016 1
< 0.1%
849.2063492 1
< 0.1%
935.059761 1
< 0.1%
1153.174603 1
< 0.1%
1188.095238 1
< 0.1%
1560.309524 1
< 0.1%
1729.063492 1
< 0.1%
1755.952381 1
< 0.1%
1777.777778 1
< 0.1%
ValueCountFrequency (%)
460495926.5 1
< 0.1%
107475531.3 1
< 0.1%
60843084.92 1
< 0.1%
60546926.98 1
< 0.1%
56703451.59 1
< 0.1%
53449109.52 1
< 0.1%
53207215.08 1
< 0.1%
52205071.83 1
< 0.1%
49901964.06 1
< 0.1%
46498527.38 1
< 0.1%

Volume 1 month
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4535
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1717373.4
Minimum4.5454545
Maximum3.4773365 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.0 KiB
2024-08-12T20:26:16.086464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4.5454545
5-th percentile8890.4348
Q188682.609
median422152.17
Q31297245.7
95-th percentile6706690.7
Maximum3.4773365 × 108
Range3.4773364 × 108
Interquartile range (IQR)1208563

Descriptive statistics

Standard deviation7224936.6
Coefficient of variation (CV)4.2069689
Kurtosis1181.4833
Mean1717373.4
Median Absolute Deviation (MAD)389656.24
Skewness27.489452
Sum7.8861788 × 109
Variance5.2199709 × 1013
MonotonicityNot monotonic
2024-08-12T20:26:16.191838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52026.08696 3
 
0.1%
81686.95652 3
 
0.1%
531769.5652 2
 
< 0.1%
216565.2174 2
 
< 0.1%
25865.21739 2
 
< 0.1%
172943.4783 2
 
< 0.1%
2600 2
 
< 0.1%
10217.3913 2
 
< 0.1%
7100 2
 
< 0.1%
54586.95652 2
 
< 0.1%
Other values (4525) 4570
99.5%
ValueCountFrequency (%)
4.545454545 1
< 0.1%
65.2173913 1
< 0.1%
73.91304348 1
< 0.1%
139.1304348 1
< 0.1%
181.8181818 1
< 0.1%
182.6086957 1
< 0.1%
213.0434783 1
< 0.1%
217.3913043 2
< 0.1%
247.9130435 1
< 0.1%
362.3043478 1
< 0.1%
ValueCountFrequency (%)
347733646.8 1
< 0.1%
110366273.9 1
< 0.1%
108802565.2 1
< 0.1%
81539721.74 1
< 0.1%
78406543.48 1
< 0.1%
63759239.13 1
< 0.1%
60902782.61 1
< 0.1%
59622121.74 1
< 0.1%
58253669.57 1
< 0.1%
53438513.04 1
< 0.1%

52 Weeks High
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3579
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.564379
Minimum0.87
Maximum14400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.0 KiB
2024-08-12T20:26:16.304143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.87
5-th percentile2.321
Q18.575
median21.36
Q359.865
95-th percentile245.8895
Maximum14400
Range14399.13
Interquartile range (IQR)51.29

Descriptive statistics

Standard deviation287.98921
Coefficient of variation (CV)4.2624414
Kurtosis1525.7195
Mean67.564379
Median Absolute Deviation (MAD)16.64
Skewness34.209996
Sum310255.63
Variance82937.782
MonotonicityNot monotonic
2024-08-12T20:26:16.415489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 8
 
0.2%
2.05 8
 
0.2%
14 7
 
0.2%
3.6 7
 
0.2%
2.1 7
 
0.2%
12 6
 
0.1%
3.5 6
 
0.1%
4 6
 
0.1%
7.88 5
 
0.1%
32 5
 
0.1%
Other values (3569) 4527
98.6%
ValueCountFrequency (%)
0.87 1
< 0.1%
0.8999 2
< 0.1%
0.9039 1
< 0.1%
0.909 1
< 0.1%
0.93 1
< 0.1%
0.94 1
< 0.1%
0.98 2
< 0.1%
0.99 1
< 0.1%
1.01 1
< 0.1%
1.02 1
< 0.1%
ValueCountFrequency (%)
14400 1
< 0.1%
8700 1
< 0.1%
4144.32 1
< 0.1%
3242.54 1
< 0.1%
2173.01 1
< 0.1%
1905.09 1
< 0.1%
1899.21 1
< 0.1%
1759.76 1
< 0.1%
1670.24 1
< 0.1%
1535.86 1
< 0.1%

52 Weeks Low
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3347
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.013462
Minimum0.0004
Maximum5210.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.0 KiB
2024-08-12T20:26:16.519895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0004
5-th percentile0.43619
Q12.16
median10.24
Q332.335
95-th percentile144.9145
Maximum5210.49
Range5210.4896
Interquartile range (IQR)30.175

Descriptive statistics

Standard deviation120.44169
Coefficient of variation (CV)3.344352
Kurtosis840.57527
Mean36.013462
Median Absolute Deviation (MAD)9.14
Skewness23.193785
Sum165373.82
Variance14506.202
MonotonicityNot monotonic
2024-08-12T20:26:16.623338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.75 11
 
0.2%
1 10
 
0.2%
1.04 9
 
0.2%
1.1 9
 
0.2%
0.7 9
 
0.2%
1.21 9
 
0.2%
0.65 9
 
0.2%
1.25 8
 
0.2%
1.03 8
 
0.2%
1.55 8
 
0.2%
Other values (3337) 4502
98.0%
ValueCountFrequency (%)
0.0004 1
< 0.1%
0.038 1
< 0.1%
0.048 1
< 0.1%
0.056 1
< 0.1%
0.064 1
< 0.1%
0.07 1
< 0.1%
0.0712 1
< 0.1%
0.0734 1
< 0.1%
0.0771 1
< 0.1%
0.0811 1
< 0.1%
ValueCountFrequency (%)
5210.49 1
< 0.1%
2735.3 1
< 0.1%
2379.02 1
< 0.1%
1401.0101 1
< 0.1%
1295.65 1
< 0.1%
1274.91 1
< 0.1%
1141.04 1
< 0.1%
930.72 1
< 0.1%
860.1 1
< 0.1%
811.99 1
< 0.1%

Exchange
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size279.4 KiB
NASDAQ
2957 
NYSE
1635 

Length

Max length6
Median length6
Mean length5.287892
Min length4

Characters and Unicode

Total characters24282
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNASDAQ
2nd rowNASDAQ
3rd rowNASDAQ
4th rowNASDAQ
5th rowNASDAQ

Common Values

ValueCountFrequency (%)
NASDAQ 2957
64.4%
NYSE 1635
35.6%

Length

2024-08-12T20:26:16.719623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T20:26:16.820193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nasdaq 2957
64.4%
nyse 1635
35.6%

Most occurring characters

ValueCountFrequency (%)
A 5914
24.4%
N 4592
18.9%
S 4592
18.9%
D 2957
12.2%
Q 2957
12.2%
Y 1635
 
6.7%
E 1635
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24282
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 5914
24.4%
N 4592
18.9%
S 4592
18.9%
D 2957
12.2%
Q 2957
12.2%
Y 1635
 
6.7%
E 1635
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24282
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 5914
24.4%
N 4592
18.9%
S 4592
18.9%
D 2957
12.2%
Q 2957
12.2%
Y 1635
 
6.7%
E 1635
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24282
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 5914
24.4%
N 4592
18.9%
S 4592
18.9%
D 2957
12.2%
Q 2957
12.2%
Y 1635
 
6.7%
E 1635
 
6.7%

Performance (52 weeks)
Real number (ℝ)

HIGH CORRELATION 

Distinct4585
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.011139993
Minimum-0.99985424
Maximum33.280986
Zeros0
Zeros (%)0.0%
Negative2391
Negative (%)52.1%
Memory size36.0 KiB
2024-08-12T20:26:16.911613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.99985424
5-th percentile-0.85974348
Q1-0.36866033
median-0.020458181
Q30.2198746
95-th percentile0.79846204
Maximum33.280986
Range34.28084
Interquartile range (IQR)0.58853493

Descriptive statistics

Standard deviation0.79557413
Coefficient of variation (CV)-71.416036
Kurtosis679.55329
Mean-0.011139993
Median Absolute Deviation (MAD)0.28301388
Skewness17.714339
Sum-51.154847
Variance0.63293819
MonotonicityNot monotonic
2024-08-12T20:26:17.123652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.5009512193 2
 
< 0.1%
0.07029265643 2
 
< 0.1%
-0.06056350255 2
 
< 0.1%
-0.47592853 2
 
< 0.1%
-0.1670839982 2
 
< 0.1%
0.387608379 2
 
< 0.1%
-0.8042474026 2
 
< 0.1%
0.2113597329 1
 
< 0.1%
-0.7288121977 1
 
< 0.1%
-0.2850604216 1
 
< 0.1%
Other values (4575) 4575
99.6%
ValueCountFrequency (%)
-0.9998542374 1
< 0.1%
-0.9994216567 1
< 0.1%
-0.9992965079 1
< 0.1%
-0.9982727312 1
< 0.1%
-0.9979352051 1
< 0.1%
-0.9973795645 1
< 0.1%
-0.9973444564 1
< 0.1%
-0.9966251513 1
< 0.1%
-0.9965653988 1
< 0.1%
-0.9964575223 1
< 0.1%
ValueCountFrequency (%)
33.28098556 1
< 0.1%
8.725687571 1
< 0.1%
7.564530876 1
< 0.1%
7.492914449 1
< 0.1%
6.921500924 1
< 0.1%
5.897939474 1
< 0.1%
5.736607268 1
< 0.1%
5.64065769 1
< 0.1%
5.450054284 1
< 0.1%
5.299123385 1
< 0.1%
Distinct51
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size264.7 KiB
2024-08-12T20:26:17.243344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters9184
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.3%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowCN
5th rowUS
ValueCountFrequency (%)
us 3880
84.5%
cn 185
 
4.0%
il 85
 
1.9%
gb 57
 
1.2%
ca 56
 
1.2%
sg 38
 
0.8%
hk 34
 
0.7%
bm 30
 
0.7%
ie 29
 
0.6%
ky 20
 
0.4%
Other values (41) 178
 
3.9%
2024-08-12T20:26:17.448999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 3927
42.8%
U 3906
42.5%
C 269
 
2.9%
N 199
 
2.2%
I 124
 
1.4%
G 113
 
1.2%
L 109
 
1.2%
B 100
 
1.1%
A 73
 
0.8%
K 61
 
0.7%
Other values (15) 303
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9184
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 3927
42.8%
U 3906
42.5%
C 269
 
2.9%
N 199
 
2.2%
I 124
 
1.4%
G 113
 
1.2%
L 109
 
1.2%
B 100
 
1.1%
A 73
 
0.8%
K 61
 
0.7%
Other values (15) 303
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9184
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 3927
42.8%
U 3906
42.5%
C 269
 
2.9%
N 199
 
2.2%
I 124
 
1.4%
G 113
 
1.2%
L 109
 
1.2%
B 100
 
1.1%
A 73
 
0.8%
K 61
 
0.7%
Other values (15) 303
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9184
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 3927
42.8%
U 3906
42.5%
C 269
 
2.9%
N 199
 
2.2%
I 124
 
1.4%
G 113
 
1.2%
L 109
 
1.2%
B 100
 
1.1%
A 73
 
0.8%
K 61
 
0.7%
Other values (15) 303
 
3.3%

Résultat net
Real number (ℝ)

HIGH CORRELATION 

Distinct4555
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6736974 × 108
Minimum-2.1601255 × 1010
Maximum1.2241905 × 1011
Zeros0
Zeros (%)0.0%
Negative2163
Negative (%)47.1%
Memory size36.0 KiB
2024-08-12T20:26:17.562659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2.1601255 × 1010
5-th percentile-2.9138745 × 108
Q1-36771286
median2553906.5
Q31.4112199 × 108
95-th percentile1.94895 × 109
Maximum1.2241905 × 1011
Range1.4402031 × 1011
Interquartile range (IQR)1.7789328 × 108

Descriptive statistics

Standard deviation4.028534 × 109
Coefficient of variation (CV)8.6195867
Kurtosis450.78781
Mean4.6736974 × 108
Median Absolute Deviation (MAD)67461500
Skewness19.222911
Sum2.1461619 × 1012
Variance1.6229086 × 1019
MonotonicityNot monotonic
2024-08-12T20:26:17.676637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1052000000 3
 
0.1%
714000000 3
 
0.1%
-40105000 2
 
< 0.1%
1560000000 2
 
< 0.1%
975000000 2
 
< 0.1%
-9636000 2
 
< 0.1%
161000000 2
 
< 0.1%
107048000 2
 
< 0.1%
164000000 2
 
< 0.1%
-35390000 2
 
< 0.1%
Other values (4545) 4570
99.5%
ValueCountFrequency (%)
-2.160125542 × 10101
< 0.1%
-1.176899994 × 10101
< 0.1%
-9406706688 1
< 0.1%
-6808999936 1
< 0.1%
-6541000192 1
< 0.1%
-5866999808 1
< 0.1%
-5810999808 1
< 0.1%
-5791000064 1
< 0.1%
-5783000064 1
< 0.1%
-4943179776 1
< 0.1%
ValueCountFrequency (%)
1.224190525 × 10111
< 0.1%
1.019560018 × 10111
< 0.1%
8.813599949 × 10101
< 0.1%
8.765699686 × 10101
< 0.1%
7.992333926 × 10101
< 0.1%
7.974100173 × 10101
< 0.1%
5.221699994 × 10101
< 0.1%
5.143400038 × 10101
< 0.1%
4.441899827 × 10101
< 0.1%
4.259799859 × 10101
< 0.1%

Sector
Categorical

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size314.9 KiB
Healthcare
1012 
Financial Services
784 
Technology
683 
Industrials
545 
Consumer Cyclical
504 
Other values (6)
1064 

Length

Max length22
Median length18
Mean length13.186847
Min length6

Characters and Unicode

Total characters60554
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndustrials
2nd rowHealthcare
3rd rowConsumer Cyclical
4th rowHealthcare
5th rowHealthcare

Common Values

ValueCountFrequency (%)
Healthcare 1012
22.0%
Financial Services 784
17.1%
Technology 683
14.9%
Industrials 545
11.9%
Consumer Cyclical 504
11.0%
Real Estate 233
 
5.1%
Consumer Defensive 207
 
4.5%
Communication Services 201
 
4.4%
Energy 179
 
3.9%
Basic Materials 158
 
3.4%

Length

2024-08-12T20:26:17.796951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
healthcare 1012
15.2%
services 985
14.7%
financial 784
11.7%
consumer 711
10.6%
technology 683
10.2%
industrials 545
8.2%
cyclical 504
7.5%
real 233
 
3.5%
estate 233
 
3.5%
defensive 207
 
3.1%
Other values (5) 782
11.7%

Most occurring characters

ValueCountFrequency (%)
e 6898
11.4%
a 5782
 
9.5%
c 4831
 
8.0%
i 4785
 
7.9%
l 4509
 
7.4%
n 4295
 
7.1%
s 3628
 
6.0%
r 3590
 
5.9%
t 2554
 
4.2%
o 2479
 
4.1%
Other values (21) 17203
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60554
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6898
11.4%
a 5782
 
9.5%
c 4831
 
8.0%
i 4785
 
7.9%
l 4509
 
7.4%
n 4295
 
7.1%
s 3628
 
6.0%
r 3590
 
5.9%
t 2554
 
4.2%
o 2479
 
4.1%
Other values (21) 17203
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60554
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6898
11.4%
a 5782
 
9.5%
c 4831
 
8.0%
i 4785
 
7.9%
l 4509
 
7.4%
n 4295
 
7.1%
s 3628
 
6.0%
r 3590
 
5.9%
t 2554
 
4.2%
o 2479
 
4.1%
Other values (21) 17203
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60554
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6898
11.4%
a 5782
 
9.5%
c 4831
 
8.0%
i 4785
 
7.9%
l 4509
 
7.4%
n 4295
 
7.1%
s 3628
 
6.0%
r 3590
 
5.9%
t 2554
 
4.2%
o 2479
 
4.1%
Other values (21) 17203
28.4%
Distinct144
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size343.2 KiB
2024-08-12T20:26:18.001742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length40
Median length32
Mean length19.508711
Min length4

Characters and Unicode

Total characters89584
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowIndustrial Distribution
2nd rowBiotechnology
3rd rowApparel Manufacturing
4th rowHealth Information Services
5th rowMedical Devices
ValueCountFrequency (%)
2262
 
18.9%
biotechnology 576
 
4.8%
services 443
 
3.7%
software 348
 
2.9%
banks 321
 
2.7%
regional 316
 
2.6%
specialty 296
 
2.5%
medical 235
 
2.0%
application 207
 
1.7%
equipment 196
 
1.6%
Other values (189) 6745
56.5%
2024-08-12T20:26:18.340911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 8492
 
9.5%
7353
 
8.2%
i 6746
 
7.5%
t 6013
 
6.7%
a 5888
 
6.6%
n 5837
 
6.5%
o 5265
 
5.9%
r 4560
 
5.1%
s 4429
 
4.9%
c 4195
 
4.7%
Other values (38) 30806
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 89584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8492
 
9.5%
7353
 
8.2%
i 6746
 
7.5%
t 6013
 
6.7%
a 5888
 
6.6%
n 5837
 
6.5%
o 5265
 
5.9%
r 4560
 
5.1%
s 4429
 
4.9%
c 4195
 
4.7%
Other values (38) 30806
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 89584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8492
 
9.5%
7353
 
8.2%
i 6746
 
7.5%
t 6013
 
6.7%
a 5888
 
6.6%
n 5837
 
6.5%
o 5265
 
5.9%
r 4560
 
5.1%
s 4429
 
4.9%
c 4195
 
4.7%
Other values (38) 30806
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 89584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8492
 
9.5%
7353
 
8.2%
i 6746
 
7.5%
t 6013
 
6.7%
a 5888
 
6.6%
n 5837
 
6.5%
o 5265
 
5.9%
r 4560
 
5.1%
s 4429
 
4.9%
c 4195
 
4.7%
Other values (38) 30806
34.4%

Price 52 Weeks Ago
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3667
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.221743
Minimum0.30000001
Maximum11250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.0 KiB
2024-08-12T20:26:18.460993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.30000001
5-th percentile1.4455001
Q15.9437501
median15.192898
Q344.50257
95-th percentile180.64417
Maximum11250
Range11249.7
Interquartile range (IQR)38.55882

Descriptive statistics

Standard deviation223.61334
Coefficient of variation (CV)4.4525204
Kurtosis1516.2916
Mean50.221743
Median Absolute Deviation (MAD)12.242898
Skewness33.946226
Sum230618.24
Variance50002.925
MonotonicityNot monotonic
2024-08-12T20:26:18.563118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.049999952 8
 
0.2%
1.679999948 8
 
0.2%
1.220000029 8
 
0.2%
10.69999981 8
 
0.2%
3.019999981 7
 
0.2%
4.199999809 7
 
0.2%
12 7
 
0.2%
1.600000024 6
 
0.1%
1.730000019 6
 
0.1%
1.110000014 6
 
0.1%
Other values (3657) 4521
98.5%
ValueCountFrequency (%)
0.3000000119 1
< 0.1%
0.400000006 1
< 0.1%
0.4300000072 1
< 0.1%
0.4379999936 1
< 0.1%
0.4799999893 1
< 0.1%
0.5009999871 1
< 0.1%
0.5099999905 1
< 0.1%
0.5170000196 1
< 0.1%
0.5210062861 1
< 0.1%
0.5320000052 1
< 0.1%
ValueCountFrequency (%)
11250 1
< 0.1%
6156.72998 1
< 0.1%
3456 1
< 0.1%
3190.70166 1
< 0.1%
2483.830078 1
< 0.1%
1566.987183 1
< 0.1%
1506.199951 1
< 0.1%
1464.240601 1
< 0.1%
1330 1
< 0.1%
1239.800049 1
< 0.1%

Currency
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct20
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size269.2 KiB
USD
4398 
CNY
 
101
EUR
 
31
BRL
 
11
CAD
 
11
Other values (15)
 
40

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13776
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 4398
95.8%
CNY 101
 
2.2%
EUR 31
 
0.7%
BRL 11
 
0.2%
CAD 11
 
0.2%
GBP 8
 
0.2%
AUD 5
 
0.1%
CHF 4
 
0.1%
SGD 4
 
0.1%
JPY 4
 
0.1%
Other values (10) 15
 
0.3%

Length

2024-08-12T20:26:18.654266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usd 4398
95.8%
cny 101
 
2.2%
eur 31
 
0.7%
brl 11
 
0.2%
cad 11
 
0.2%
gbp 8
 
0.2%
aud 5
 
0.1%
chf 4
 
0.1%
sgd 4
 
0.1%
jpy 4
 
0.1%
Other values (10) 15
 
0.3%

Most occurring characters

ValueCountFrequency (%)
U 4434
32.2%
D 4421
32.1%
S 4403
32.0%
C 116
 
0.8%
Y 108
 
0.8%
N 107
 
0.8%
R 50
 
0.4%
E 33
 
0.2%
B 19
 
0.1%
A 17
 
0.1%
Other values (13) 68
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13776
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 4434
32.2%
D 4421
32.1%
S 4403
32.0%
C 116
 
0.8%
Y 108
 
0.8%
N 107
 
0.8%
R 50
 
0.4%
E 33
 
0.2%
B 19
 
0.1%
A 17
 
0.1%
Other values (13) 68
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13776
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 4434
32.2%
D 4421
32.1%
S 4403
32.0%
C 116
 
0.8%
Y 108
 
0.8%
N 107
 
0.8%
R 50
 
0.4%
E 33
 
0.2%
B 19
 
0.1%
A 17
 
0.1%
Other values (13) 68
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13776
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 4434
32.2%
D 4421
32.1%
S 4403
32.0%
C 116
 
0.8%
Y 108
 
0.8%
N 107
 
0.8%
R 50
 
0.4%
E 33
 
0.2%
B 19
 
0.1%
A 17
 
0.1%
Other values (13) 68
 
0.5%

Total assets
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4475
Distinct (%)99.8%
Missing108
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean1.6801947 × 108
Minimum1024
Maximum1.52041 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.0 KiB
2024-08-12T20:26:18.745970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1024
5-th percentile3521482
Q118065700
median50876600
Q31.3168025 × 108
95-th percentile6.0919052 × 108
Maximum1.52041 × 1010
Range1.5204099 × 1010
Interquartile range (IQR)1.1361455 × 108

Descriptive statistics

Standard deviation5.3726484 × 108
Coefficient of variation (CV)3.1976345
Kurtosis225.91437
Mean1.6801947 × 108
Median Absolute Deviation (MAD)40574850
Skewness12.24925
Sum7.533993 × 1011
Variance2.8865351 × 1017
MonotonicityNot monotonic
2024-08-12T20:26:18.854237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132670000 2
 
< 0.1%
100625000 2
 
< 0.1%
144976992 2
 
< 0.1%
46608800 2
 
< 0.1%
37457200 2
 
< 0.1%
28483600 2
 
< 0.1%
14000000 2
 
< 0.1%
116454000 2
 
< 0.1%
69067000 2
 
< 0.1%
25318800 1
 
< 0.1%
Other values (4465) 4465
97.2%
(Missing) 108
 
2.4%
ValueCountFrequency (%)
1024 1
< 0.1%
12443 1
< 0.1%
113809 1
< 0.1%
164495 1
< 0.1%
228025 1
< 0.1%
333008 1
< 0.1%
360600 1
< 0.1%
376141 1
< 0.1%
393449 1
< 0.1%
396368 1
< 0.1%
ValueCountFrequency (%)
1.52041001 × 10101
< 0.1%
1.049559962 × 10101
< 0.1%
8043539968 1
< 0.1%
7759580160 1
< 0.1%
7433039872 1
< 0.1%
7170240000 1
< 0.1%
6478000000 1
< 0.1%
6365200000 1
< 0.1%
5858999808 1
< 0.1%
5666699776 1
< 0.1%

EPS Annual
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4507
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.12150327
Minimum-2997.8
Maximum11880.286
Zeros0
Zeros (%)0.0%
Negative2115
Negative (%)46.1%
Memory size36.0 KiB
2024-08-12T20:26:18.965091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2997.8
5-th percentile-9.597185
Q1-1.14425
median0.14605
Q32.285825
95-th percentile9.699825
Maximum11880.286
Range14878.086
Interquartile range (IQR)3.430075

Descriptive statistics

Standard deviation194.54043
Coefficient of variation (CV)-1601.1127
Kurtosis3054.6241
Mean-0.12150327
Median Absolute Deviation (MAD)1.6405
Skewness48.343212
Sum-557.943
Variance37845.977
MonotonicityNot monotonic
2024-08-12T20:26:19.158941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6003 3
 
0.1%
-0.0212 3
 
0.1%
1.6387 2
 
< 0.1%
-3.5681 2
 
< 0.1%
-0.2602 2
 
< 0.1%
-0.398 2
 
< 0.1%
1.8876 2
 
< 0.1%
0.0466 2
 
< 0.1%
-0.1822 2
 
< 0.1%
-0.0216 2
 
< 0.1%
Other values (4497) 4570
99.5%
ValueCountFrequency (%)
-2997.8 1
< 0.1%
-2181 1
< 0.1%
-1952.8796 1
< 0.1%
-1586.1632 1
< 0.1%
-1564.215 1
< 0.1%
-1018.2512 1
< 0.1%
-1008.1846 1
< 0.1%
-666.5292 1
< 0.1%
-471.8591 1
< 0.1%
-434.6842 1
< 0.1%
ValueCountFrequency (%)
11880.2857 1
< 0.1%
2241.184 1
< 0.1%
788.6044 1
< 0.1%
463.3511 1
< 0.1%
201.4798 1
< 0.1%
149.2047 1
< 0.1%
133.0526 1
< 0.1%
117.4103 1
< 0.1%
92.8812 1
< 0.1%
77.818 1
< 0.1%

ROI Annual
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3386
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-68.822545
Minimum-108880
Maximum12633.08
Zeros0
Zeros (%)0.0%
Negative2092
Negative (%)45.6%
Memory size36.0 KiB
2024-08-12T20:26:19.266890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-108880
5-th percentile-162.166
Q1-26.4825
median1.365
Q38.26
95-th percentile24.0535
Maximum12633.08
Range121513.08
Interquartile range (IQR)34.7425

Descriptive statistics

Standard deviation1799.4173
Coefficient of variation (CV)-26.145754
Kurtosis2992.8376
Mean-68.822545
Median Absolute Deviation (MAD)10.57
Skewness-51.82124
Sum-316033.13
Variance3237902.8
MonotonicityNot monotonic
2024-08-12T20:26:19.370101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.15 7
 
0.2%
4.53 7
 
0.2%
4.19 7
 
0.2%
9.32 6
 
0.1%
3.2 6
 
0.1%
1.17 6
 
0.1%
3.7 6
 
0.1%
2.06 5
 
0.1%
9.29 5
 
0.1%
0.64 5
 
0.1%
Other values (3376) 4532
98.7%
ValueCountFrequency (%)
-108880 1
< 0.1%
-41952.85 1
< 0.1%
-29104.88 1
< 0.1%
-9780 1
< 0.1%
-5196.477 1
< 0.1%
-5033.59 1
< 0.1%
-3715.82 1
< 0.1%
-3645.8 1
< 0.1%
-3304.87 1
< 0.1%
-3184.29 1
< 0.1%
ValueCountFrequency (%)
12633.08 1
< 0.1%
2662.67 1
< 0.1%
1875.53 1
< 0.1%
1670 1
< 0.1%
1054.47 1
< 0.1%
737.69 1
< 0.1%
643.41 1
< 0.1%
445.65 1
< 0.1%
432.25 1
< 0.1%
307.73 1
< 0.1%

Interactions

2024-08-12T20:26:12.147357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:56.674087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:57.786193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:59.076884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:00.263088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:01.539883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:02.799407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:04.187364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:05.388336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:06.592943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:08.570534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:09.683579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:10.964602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:12.228125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:56.759217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:57.873504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:59.159570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:00.348629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:01.633057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:02.889484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:04.269971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:05.472148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:06.776188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:08.649039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:09.764151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:11.044374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:12.318717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:56.848474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:57.966442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:59.252273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:00.442403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:01.734033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:02.992127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:04.363013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:05.567333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:06.874438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:08.738405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:09.851450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:11.139506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:12.403009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:56.927874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:58.058046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:59.338825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:00.530164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:01.829458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:03.083459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:04.452774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:05.657506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:06.964809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:08.823758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:09.938618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:11.228728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:12.484576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:57.007263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:58.148746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:59.421938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:00.616180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:01.919018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:03.177219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:04.541304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:05.747241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:07.054501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:08.909856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:10.028226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:11.318977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:12.577315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:57.093415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:58.247345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:59.514556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:00.715459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:02.015388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:03.283509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:04.641551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:05.844606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:07.155071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:09.001157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:10.228402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:11.420610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:12.667598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:57.179807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:58.345213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:59.603297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:00.815635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:02.116639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:03.387841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:04.738705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:05.941901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:07.251998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:09.093745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:10.325769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:11.517174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:12.753322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:57.259402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:58.437324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:59.689296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:00.903412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:02.209522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:03.482839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:04.829719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:06.028975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:08.022703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:09.174473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:10.416749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:11.606300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:12.934082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:57.342679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:58.530311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:59.781978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:00.992629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:02.310159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:03.584208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:04.920244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:06.118768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:08.113485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:09.259498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:10.510807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:11.695598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:13.025983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:57.432927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:58.710307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:59.881979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:01.089760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:02.407510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:03.690353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:05.014204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:06.218852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:08.205863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:09.349011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:10.608621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:11.787805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:13.109316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:57.515364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:58.802269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:59.971439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:01.179305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:02.500361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:03.785748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:05.100071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:06.310380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:08.294404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:09.430138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:10.699952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:11.875353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:13.201060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:57.608514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:58.899638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:00.071778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:01.269399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:02.602755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:03.990468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:05.198266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:06.408462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:08.389400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:09.517944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:10.789166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:11.970263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:13.290488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:57.700022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:25:58.992478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:00.171413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:01.455474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:02.703742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:04.089618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:05.302764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:06.503315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:08.482941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:09.604614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:10.881073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:26:12.061201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-08-12T20:26:19.467328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
52 Weeks High52 Weeks LowBetaCurrencyEPS AnnualExchangeMarket Cap (in M)Performance (52 weeks)PricePrice 52 Weeks AgoROI AnnualRésultat netSectorTotal assetsVolume 1 monthVolume 52 weeks
52 Weeks High1.0000.896-0.0280.0000.5580.0150.7320.3620.9040.9650.5230.5490.0000.2060.2800.228
52 Weeks Low0.8961.000-0.1260.0000.7070.0180.8300.4850.9830.9110.6590.6530.0000.2850.2250.167
Beta-0.028-0.1261.0000.000-0.2460.0910.037-0.068-0.088-0.062-0.267-0.2520.1250.1550.2490.272
Currency0.0000.0000.0001.0000.5610.0000.0000.0000.0000.0000.0000.3530.0580.0000.0000.000
EPS Annual0.5580.707-0.2460.5611.0000.0850.6000.4000.6820.5840.8520.8340.0520.2450.1170.075
Exchange0.0150.0180.0910.0000.0851.0000.0470.0420.0270.0000.0190.0740.4160.0750.0000.000
Market Cap (in M)0.7320.8300.0370.0000.6000.0471.0000.5130.8450.7280.5390.5580.0230.7140.6010.566
Performance (52 weeks)0.3620.485-0.0680.0000.4000.0420.5131.0000.5790.2470.3680.3870.0150.2060.0770.045
Price0.9040.983-0.0880.0000.6820.0270.8450.5791.0000.8930.6320.6330.0000.2920.2460.185
Price 52 Weeks Ago0.9650.911-0.0620.0000.5840.0000.7280.2470.8931.0000.5490.5610.0200.2160.2690.208
ROI Annual0.5230.659-0.2670.0000.8520.0190.5390.3680.6320.5491.0000.7420.0410.1900.0810.041
Résultat net0.5490.653-0.2520.3530.8340.0740.5580.3870.6330.5610.7421.0000.0320.2200.1530.119
Sector0.0000.0000.1250.0580.0520.4160.0230.0150.0000.0200.0410.0321.0000.0380.0000.016
Total assets0.2060.2850.1550.0000.2450.0750.7140.2060.2920.2160.1900.2200.0381.0000.7710.780
Volume 1 month0.2800.2250.2490.0000.1170.0000.6010.0770.2460.2690.0810.1530.0000.7711.0000.942
Volume 52 weeks0.2280.1670.2720.0000.0750.0000.5660.0450.1850.2080.0410.1190.0160.7800.9421.000

Missing values

2024-08-12T20:26:13.428516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-12T20:26:13.666790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SymbolCompany NamePriceMarket Cap (in M)BetaVolume 52 weeksVolume 1 month52 Weeks High52 Weeks LowExchangePerformance (52 weeks)CountryRésultat netSectorIndustryPrice 52 Weeks AgoCurrencyTotal assetsEPS AnnualROI Annual
0TRNSTranscat Inc116.29001051.5526920.9319895.090984e+047.298288e+04147.11584.450NASDAQ0.211360US15106000.0IndustrialsIndustrial Distribution96.050003USD2.553100e+071.63405.95
1ACRVAcrivon Therapeutics Inc7.1800217.9911340.7118532.340076e+057.426779e+0412.8503.190NASDAQ-0.381850US-64118000.0HealthcareBiotechnology11.600000USD1.320950e+08-2.7352-49.83
2COLMColumbia Sportswear Co81.83504781.3574870.6283734.619039e+055.568954e+0587.23066.010NASDAQ0.098148US227407008.0Consumer CyclicalApparel Manufacturing74.540024USD1.250472e+094.092912.97
3ZCMDZhongchao Inc1.490012.008726-1.4798833.614145e+051.164410e+0612.0001.000NASDAQ-0.873369CN-11335911.0HealthcareHealth Information Services11.700000USDNaN-4.3550-62.95
4MOVEMovano Inc0.371436.5252241.2164911.513081e+052.487234e+051.4000.266NASDAQ-0.715289US-27907000.0HealthcareMedical Devices1.300000USD3.505800e+07-0.6339-837.14
5NMIHNMI Holdings Inc37.24002955.0584240.6787915.305636e+055.543996e+0542.00025.640NASDAQ0.280151US348496992.0Financial ServicesInsurance - Specialty29.110001USDNaN3.841313.86
6GNTAGenenta Science SPA4.563581.703059-1.1924995.514391e+032.803921e+046.1002.200NASDAQ-0.199875IT-11645455.0HealthcareBiotechnology5.700000EURNaN-0.6393-57.00
7ALLRAllarity Therapeutics Inc0.14266.2043603.2902102.291709e+069.179877e+0646.6000.138NASDAQ-0.996625US-12823000.0HealthcareBiotechnology41.599998USDNaN-119.6080-285.88
8RRBIRed River Bancshares Inc49.3300341.0500250.6513409.616577e+031.154975e+0458.00042.780NASDAQ-0.004805US32488000.0Financial ServicesBanks - Regional49.567513USDNaN4.856611.48
9HCKTHackett Group Inc25.5550711.3564920.4041379.765614e+041.270293e+0527.68020.230NASDAQ0.090217US34749000.0TechnologyInformation Technology Services23.445827USD5.962300e+071.235727.81
SymbolCompany NamePriceMarket Cap (in M)BetaVolume 52 weeksVolume 1 month52 Weeks High52 Weeks LowExchangePerformance (52 weeks)CountryRésultat netSectorIndustryPrice 52 Weeks AgoCurrencyTotal assetsEPS AnnualROI Annual
4582BOXBox Inc27.514002.3695710.4103471.836657e+061.355296e+0630.9723.5650NYSE-0.094119US1.070040e+08TechnologySoftware - Infrastructure30.360001USD144976992.00.868429.88
4583NSCNorfolk Southern Corp239.6254177.2276120.8009471.256250e+061.239665e+06263.66183.0900NYSE0.137450US1.791000e+09IndustrialsRailroads210.738556USD226096000.08.03436.10
4584CPACopa Holdings SA88.123673.4571441.1823493.297429e+053.127565e+05114.0078.1200NYSE-0.051162PA6.713850e+08IndustrialsAirlines92.858147USD30748900.012.779613.29
4585GPORGulfport Energy Corp138.142501.3117090.9882212.022063e+052.470174e+05165.13108.8400NYSE0.212501US7.523250e+08EnergyOil & Gas E&P113.989998USD18107100.077.818051.19
4586BABoeing Co167.91103460.6274121.1499397.051050e+066.611630e+06267.54159.7000NYSE-0.288335US-3.441000e+09IndustrialsAerospace & Defense235.720001USD616166976.0-3.667973.73
4587IVZInvesco Ltd16.167272.5241281.2013634.633188e+064.554791e+0618.2812.4800NYSE0.027753US-3.372000e+08Financial ServicesAsset Management15.724797USD450032000.0-0.2131-0.42
4588FBPFirst BanCorp19.743285.2710250.9282111.117185e+061.213457e+0622.1212.7150NYSE0.347341PR3.108070e+08Financial ServicesBanks - Regional14.663054USD163864992.01.709418.25
4589SNDASonida Senior Living Inc29.36418.1133200.8845681.800198e+042.690000e+0434.266.8900NYSE1.992714US-2.302900e+07HealthcareMedical Care Facilities9.840000USD14240900.0-3.1104-3.80
4590COHRCoherent Corp63.349656.8805423.4231482.326590e+062.209304e+0680.9128.4700NYSE0.403250US-4.145670e+08TechnologyScientific & Technical Instruments45.180000USD152460992.0-1.8859-2.24
4591TWLOTwilio Inc60.419701.8364001.5282492.782786e+062.422213e+0678.1649.8561NYSE-0.024610US-5.943220e+08TechnologySoftware - Infrastructure61.930000USD160600000.0-5.5389-9.46